A Demand-aware Networked System Using Telemetry and ML with ReactNET
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916346071089152 |
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| author | Miri, Seyed Milad Schmid, Stefan Mostafaei, Habib |
| author_facet | Miri, Seyed Milad Schmid, Stefan Mostafaei, Habib |
| contents | Emerging network applications ranging from video streaming to virtual/augmented reality need to provide stringent quality-of-service (QoS) guarantees in complex and dynamic environments with shared resources. A promising approach to meeting these requirements is to automate complex network operations and create self-adjusting networks. These networks should automatically gather contextual information, analyze how to efficiently ensure QoS requirements, and adapt accordingly. This paper presents ReactNET, a self-adjusting networked system designed to achieve this vision by leveraging emerging network programmability and machine learning techniques. Programmability empowers ReactNET by providing fine-grained telemetry information, while machine learning-based classification techniques enable the system to learn and adjust the network to changing conditions. Our preliminary implementation of ReactNET in P4 and Python demonstrates its effectiveness in video streaming applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02057 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Demand-aware Networked System Using Telemetry and ML with ReactNET Miri, Seyed Milad Schmid, Stefan Mostafaei, Habib Networking and Internet Architecture Emerging network applications ranging from video streaming to virtual/augmented reality need to provide stringent quality-of-service (QoS) guarantees in complex and dynamic environments with shared resources. A promising approach to meeting these requirements is to automate complex network operations and create self-adjusting networks. These networks should automatically gather contextual information, analyze how to efficiently ensure QoS requirements, and adapt accordingly. This paper presents ReactNET, a self-adjusting networked system designed to achieve this vision by leveraging emerging network programmability and machine learning techniques. Programmability empowers ReactNET by providing fine-grained telemetry information, while machine learning-based classification techniques enable the system to learn and adjust the network to changing conditions. Our preliminary implementation of ReactNET in P4 and Python demonstrates its effectiveness in video streaming applications. |
| title | A Demand-aware Networked System Using Telemetry and ML with ReactNET |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2408.02057 |